In March 2022, Amazon and Carnegie Mellon University announced the second class of Amazon Graduate Research fellows, marking an extension of the company’s efforts to help reinforce the work done by Master’s degree and PhD students. Now the next round of fellows has been announced.
The program, launched in 2021, supports graduate students dealing with scientific research in automated reasoning, computer vision, robotics, language technology, machine learning, operational research and data science.
This year’s class includes two new fellows and two, which were also fellows in 2022. Fellows will also be invited to interview for science internships at Amazon.
The four fellows and their focus areas are below:
Shantanu GuptaMachine Learning: Gupta is recommended by Zachary Lipton, an assistant professor in operations research and machine learning, who also works as a consultant for Amazon, and David Childers, assistant professor in finance.
He is researching the development of algorithms “for efficiently estimating a target parameter when we have access to multiple data sources (with an associated cost structure) Return a different subset of features. Under Budget Constraints, we must decide which data to query at each time step. Apply two parameters in semi-parametric models where high-dimensional nuisance parameters (eg, propensity score) need to be estimated to estimate target parameter (eg ate).
Ian Waudby-SmithStatistics: Waudby-Smith is recommended by Aaditya Ramdas, one Amazon visits academically and assistant professor in the departments of statistics and data science as well as machine learning.
His research relates to online changepoint detection -algorithms “is tasked with determining whether and when an online stream of data has had a change in distribution. For example, it may be of interest to record whether daily traffic on a site has changed significantly, or whether users’ answers to an A/B testing is not -Stationally. -the assumptions.
Emre YolcuComputer Science: Yolcus Faculty Advisor is Marijn Heule, Associate Professor of Computer Science and an Amazon. Yolcu was also a fellow in 2022; This year, his focus is automated reasoning and certificate of evidence.
“The main goal is to better understand the relative strengths of the evidence systems designed to create the next step in the development of today’s SAT -SOLDS by facilitating effective evidence in evidence systems stronger than dissolution (the evidence system underlying CDCL -soliders). Achieve this goal, we develop elegant and modular proof techniques that allow us to prove separations of these system, Which ultimately helps us prioritize us with proof of proof of proof that has proof of proof of proof of proof that has a proof of evidence has the ideal ideas of the idea of the ideal, the ideal ideal balance between expressiveness in theory and easy evidence in practice.
Minji YoonComputer Science: Yoon, who was also a fellow in 2022, is advised by Christos Faloutsos, Fredkin Professor of Computer Science and an Amazon, and Ruslan Salakhutdinov, professor of computer science. She is focused on deep learning on graphs.
“The goal of this thesis is to do deep learning on graphs (DLG) more practical. To have a broader influence, we need to make DLG (1) to require less handmade work, such as hyperparameter setting and (2) satisfy the real world restrictions, including scalability and privacy. Generalized Neidel transfers across different domains with at least fine -tuning and (c) a redefinition of conventional problem formulations that consider scalability and privacy in the real world.